A holistic system for troll detection on Twitter. (December 2018)
- Record Type:
- Journal Article
- Title:
- A holistic system for troll detection on Twitter. (December 2018)
- Main Title:
- A holistic system for troll detection on Twitter
- Authors:
- Fornacciari, Paolo
Mordonini, Monica
Poggi, Agostino
Sani, Laura
Tomaiuolo, Michele - Abstract:
- Abstract: Various techniques based on artificial intelligence have been proposed for the automatic detection of online anti-social behaviors, both in existing systems and in the scientific literature. In this article, we describe TrollPacifier, a holistic system for troll detection, which analyses many different features of trolls and legitimate users on the popular Twitter platform. In this system, the most known and promising approaches and research lines are applied, along with original new ideas, in a form that fits such a large public platform. In particular, we have identified six groups of features, based respectively on the analysis of writing style, sentiment, behaviors, social interactions, linked media, and publication time. As its main scientific contributions, this work provides: ( i ) an up-to-date analysis of the state of the art for the problem of troll detection; ( ii ) the systematic collection and grouping of features, on Twitter; ( iii ) the description of a working holistic system for troll detection, with a very high accuracy (95.5%); and ( iv ) a comparison among the different features, with a machine learning approach. Our results demonstrate that automatic classification can be useful in the whole process of identification and management of online anti-social behaviors. However, a multi-faceted approach is required, in order to obtain an adequate accuracy. Highlights: Up-to-date analysis of the state of the art for the problem of troll detection.Abstract: Various techniques based on artificial intelligence have been proposed for the automatic detection of online anti-social behaviors, both in existing systems and in the scientific literature. In this article, we describe TrollPacifier, a holistic system for troll detection, which analyses many different features of trolls and legitimate users on the popular Twitter platform. In this system, the most known and promising approaches and research lines are applied, along with original new ideas, in a form that fits such a large public platform. In particular, we have identified six groups of features, based respectively on the analysis of writing style, sentiment, behaviors, social interactions, linked media, and publication time. As its main scientific contributions, this work provides: ( i ) an up-to-date analysis of the state of the art for the problem of troll detection; ( ii ) the systematic collection and grouping of features, on Twitter; ( iii ) the description of a working holistic system for troll detection, with a very high accuracy (95.5%); and ( iv ) a comparison among the different features, with a machine learning approach. Our results demonstrate that automatic classification can be useful in the whole process of identification and management of online anti-social behaviors. However, a multi-faceted approach is required, in order to obtain an adequate accuracy. Highlights: Up-to-date analysis of the state of the art for the problem of troll detection. Systematic collection and grouping of features, on Twitter. Construction of a working holistic system, with a very high accuracy (95.5%). Comparison among the different features, with a machine learning approach. … (more)
- Is Part Of:
- Computers in human behavior. Volume 89(2018)
- Journal:
- Computers in human behavior
- Issue:
- Volume 89(2018)
- Issue Display:
- Volume 89, Issue 2018 (2018)
- Year:
- 2018
- Volume:
- 89
- Issue:
- 2018
- Issue Sort Value:
- 2018-0089-2018-0000
- Page Start:
- 258
- Page End:
- 268
- Publication Date:
- 2018-12
- Subjects:
- Troll detection -- Machine learning -- Online social networks
Interactive computer systems -- Periodicals
Man-machine systems -- Periodicals
004.019 - Journal URLs:
- http://www.sciencedirect.com/science/journal/07475632 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.chb.2018.08.008 ↗
- Languages:
- English
- ISSNs:
- 0747-5632
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 3394.921600
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 25802.xml